Cleyton Magalhaes

Ronnie de Souza Santos, Maria Teresa Baldassarre, Cleyton Magalhaes et al.

Large language models are increasingly becoming part of software engineering education, including activities involving empirical software engineering and evidence synthesis. This paper reports an educational experience involving the integration of reflective LLM use into an empirical methods assignment in a third-year software architecture course. Students were asked to develop a short research paper using either a rapid review or a gray literature review methodology and to disclose how LLMs were used throughout the assignment. We analyzed 146 student disclosure statements using a cross-analysis process combining LLM-assisted categorization with manual verification and refinement by the researchers. The reflections describe how students incorporated LLMs during activities such as brainstorming, methodological clarification, organization of findings, and writing refinement, while also reporting concerns regarding inaccuracies and verification of generated content. This experience report discusses lessons learned and educational implications for integrating AI-assisted technologies into empirical software engineering education.

Ronnie de Souza Santos, Luiz Fernando de Lima, Cleyton Magalhaes

Context. Algorithmic racism is the term used to describe the behavior of technological solutions that constrains users based on their ethnicity. Lately, various data-driven software systems have been reported to discriminate against Black people, either for the use of biased data sets or due to the prejudice propagated by software professionals in their code. As a result, Black people are experiencing disadvantages in accessing technology-based services, such as housing, banking, and law enforcement. Goal. This study aims to explore algorithmic racism from the perspective of software professionals. Method. A survey questionnaire was applied to explore the understanding of software practitioners on algorithmic racism, and data analysis was conducted using descriptive statistics and coding techniques. Results. We obtained answers from a sample of 73 software professionals discussing their understanding and perspectives on algorithmic racism in software development. Our results demonstrate that the effects of algorithmic racism are well-known among practitioners. However, there is no consensus on how the problem can be effectively addressed in software engineering. In this paper, some solutions to the problem are proposed based on the professionals' narratives. Conclusion. Combining technical and social strategies, including training on structural racism for software professionals, is the most promising way to address the algorithmic racism problem and its effects on the software solutions delivered to our society.

Ronnie de Souza Santos, Maria Teresa Baldassarre, Cleyton Magalhaes

The widespread integration of AI technologies has intensified concerns about fairness and bias, as these systems often perpetuate societal inequalities through flawed data and design choices. While software engineering research has largely concentrated on technical solutions, such as improving datasets and models, the social dynamics that shape AI outcomes remain underexplored. This study investigates the role of team diversity in the development of AI systems. Drawing from the experience of four AI focused teams working in a large software company operating in Brazil and Portugal, and collaborating with global clients, the study explores how diverse teams influence the development of AI systems. Using Grounded Theory, we conducted 25 interviews with software professionals involved in projects spanning domains such as education, energy, accessibility, and facial recognition. Although our study is conducted in an organizational setting, the variety of projects, from regional to multinational, ensures exposure to global development practices and diverse team dynamics, bringing a variety of perspectives into our findings. Our analysis revealed six key roles that team diversity played in AI development: diversifying perspectives for bias identification, bringing empathy to AI development, addressing systemic discrimination, supporting inclusive and participatory decision making, using diversity as a safeguard against bias, and fostering broadened thinking in problem solving. These findings highlight the importance of incorporating diverse perspectives in AI projects and offer practical recommendations for integrating fairness considerations into software development practices.

Ronnie Santos, Cleyton Magalhaes, Luiz Fernando Capretz et al.

Over the last several decades, computer games started to have a significant impact on society. However, although a computer game is a type of software, the process to conceptualize, produce and deliver a game could involve unusual features. In software testing, for instance, studies demonstrated the hesitance of professionals to use automated testing techniques with games, due to the constant changes in requirements and design, and pointed out the need for creating testing tools that take into account the flexibility required for the game development process. Goal. This study aims to improve the current body of knowledge regarding software testing in game development and point out the existing particularities observed in software testing considering the development of a computer game. Method. A mixed-method approach based on a case study and an opinion survey was applied to collect quantitative and qualitative data from software professionals regarding the particularities of software testing in game development. Results. We analyzed over 70 messages posted on three well-established network of question-and-answer communities related to software engineering, software testing and game development and received answers of 38 professionals discussing differences between testing a computer game and a general software, and identified important aspects to be observed by practitioners in the process of planning, performing and reporting tests in this context. Conclusion. Considering computer games, software testing must focus not only on the common aspects of a general software, but also, track and investigate issues that could be related to game balance, game physics and entertainment related-aspects to guarantee the quality of computer games and a successful testing process.

Ronnie Santos, Cleyton Magalhaes, Jorge Correia-Neto et al.

Context. Considering the importance of software testing to the development of high quality and reliable software systems, this paper aims to investigate how can work-related factors influence the motivation of software testers. Method. We applied a questionnaire that was developed using a previous theory of motivation and satisfaction of software engineers to conduct a survey-based study to explore and understand how professional software testers perceive and value work-related factors that could influence their motivation at work. Results. With a sample of 80 software testers we observed that software testers are strongly motivated by variety of work, creative tasks, recognition for their work, and activities that allow them to acquire new knowledge, but in general the social impact of this activity has low influence on their motivation. Conclusion. This study discusses the difference of opinions among software testers, regarding work-related factors that could impact their motivation, which can be relevant for managers and leaders in software engineering practice.